The functions of mammalian brains are based on the activity patterns of large numbers of interconnected neurons that form information processing circuits. Neural circuits consist of local connections— where pre- and post-synaptic partners reside within the same brain area and long-distance connections, which link different areas (Fig. 1). Local connections can be predicted by axon and dendrite reconstructions, and confirmed by physiological recording and stimulation methods. Long-distance connections are more difficult to map, as commonly used methods can only trace bulk projections with a coarse resolution. Most methods cannot distinguish axons in passing from those that form synapses, or pinpoint the neuronal types to which connections are made. Trans-synaptic tracers can potentially overcome these limitations. Here we combine a retrograde rabies-virus-dependent mono-trans-synaptic labeling techniquewith genetic control of the location, number and cell type of ‘starter’ neurons to trace their presynaptic partners. We systematically mapped long-distance connections between the first olfactory processing centre, the olfactory bulb, and its postsynaptic targets in the olfactory cortex including the anterior olfactory nucleus (AON), piriform cortex and amygdala.

To compare distribution of labelled glomeruli (olfactory bulb) and starter cells (AON) across different samples, we needed to map them in a common 3D reference frame. To do this, we first saved manual annotations carried out in Adobe Illustrator in a scalable vector graphics (SVG) format, making it feasible to accurately visualize and parse the information by MATLAB scripts. Each slice is represented by a series of points and the centre of mass contained within an SVG file. To assemble the slices represented by SVG files into a 3D shape, we first aligned the centre of mass for each slice to that of the previous slice to form the cylindrical (z-)axis. Then, we refined the alignment by sequentially applying the iterative closest points (ICP) algorithm, which can identify the local rotation and translation parameters for each slice to maximize the overlap with the previous slice. Once we had aligned all the slices in a sample to generate a 3D shape, we needed to align shapes between different mice samples. In this case, principle component analysis (PCA) can reliably find rotation and translation and translation to perform rigid registration. Finally, we calculated the volume occupied by each shape and applied a uniform scaling factor to account for different sizes of the anatomical structures in different animals (Fig. 2). Once we have all the anatomies registered, we can perform quantitative analysis of the spatial order between trans-synaptic connections using statistical sampling methods. In particular, given the shape of AON and olfactory bulb, elliptical coordinates are optimal to conduct such analyses.

Figure 2: Assestment of the precision of our 3D reconstruction for the olfactory bulb glomerular map using mice in which glomerular targets of a single ORN class are labeled with GFP. Last panel on the right shows the superposition of the three registered olfactory bulbs to demonstrate that corresponding labeled glomeruli are located within a distance equivalent to the diameter of several glomeruli. (From [1]).